A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses

TL;DR

This paper proposes a multi-loss approach with novel loss functions to enhance vehicle trajectory prediction, ensuring predicted paths are realistic, diverse, and compliant with traffic rules, thereby improving safety and robustness in autonomous driving.

Contribution

Introduction of three new loss functions—Offroad Loss, Direction Consistency Error, and Diversity Loss—that improve trajectory prediction accuracy and safety by enforcing realistic and diverse predictions.

Findings

Significantly reduces offroad errors by 47% on original scenes.

Enhances model robustness against adversarial attacks.

Sets a new benchmark for trajectory prediction accuracy.

Abstract

Trajectory prediction is essential for the safety and efficiency of planning in autonomous vehicles. However, current models often fail to fully capture complex traffic rules and the complete range of potential vehicle movements. Addressing these limitations, this study introduces three novel loss functions: Offroad Loss, Direction Consistency Error, and Diversity Loss. These functions are designed to keep predicted paths within driving area boundaries, aligned with traffic directions, and cover a wider variety of plausible driving scenarios. As all prediction modes should adhere to road rules and conditions, this work overcomes the shortcomings of traditional "winner takes all" training methods by applying the loss functions to all prediction modes. These loss functions not only improve model training but can also serve as metrics for evaluating the realism and diversity of trajectory predictions. Extensive validation on the nuScenes and Argoverse 2 datasets with leading baseline models demonstrates that our approach not only maintains accuracy but significantly improves safety and robustness, reducing offroad errors on average by 47% on original and by 37% on attacked scenes. This work sets a new benchmark for trajectory prediction in autonomous driving, offering substantial improvements in navigating complex environments. Our code is available at https://github.com/vita-epfl/stay-on-track .